Massive Uncoordinated Access With Massive MIMO: A Dictionary Learning Approach

Han, Yong-Hee; Rao, Bhaskar D.; Lee, Jung Woo

doi:10.1109/twc.2019.2952843

Cited by 38 publications

(16 citation statements)

References 39 publications

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“…Recently a number of non-orthogonal sequence based random access schemes for mMTC have been proposed, e.g., the two-phase grant-free random access [5], the grant-free random access with data embedding [13] or data spreading [19]. The non-orthogonal sequences can be used as signatures for active device detection, e.g., [8], [11], [20], as codewords for data transmission, e.g., [12], [21], [22], or as both, e.g., [1], [13].…”

Section: A Related Workmentioning

confidence: 99%

Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO

Chen

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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This paper considers the massive random access problem in which a large number of sporadically active devices wish to communicate with a base-station (BS) equipped with a large number of antennas. Each device is preassigned a unique signature sequence, and the BS identifies the active devices in the random access by detecting which sequences are transmitted. This device activity detection problem can be formulated as a maximum likelihood estimation (MLE) problem with the sample covariance matrix of the received signal being a sufficient statistic. The aim of this paper is to characterize the parameter space in which this covariance based approach would be able to successfully recover the device activities in the massive multipleinput multiple-output (MIMO) regime. Through an analysis of the asymptotic behaviors of MLE via its associated Fisher information matrix, this paper derives a necessary and sufficient condition on the Fisher information matrix to ensure a vanishing detection error rate as the number of antennas goes to infinity, based on which a numerical phase transition analysis is obtained. This condition is also examined from a perspective of covariance matching that relates the phase transition analysis in this paper to a recently derived scaling law. Furthermore, this paper provides a characterization of the distribution of the estimation error in MLE, based on which the error probabilities in device activity detection can be accurately predicted. Finally, this paper studies a random access scheme with joint device activity and data detection and analyzes its performance in a similar way. Simulation results validate the analysis.

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Section: A Related Workmentioning

confidence: 99%

Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO

Chen

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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“…Another paper [234] presented an uncoordinated access protocol for huge connectivity that takes advantage of a large number of antennas at the base station and is likely to be widely used in cellular networks. A large number of IoT devices can transmit data without any prior scheduling procedure using the proposed approach, which consists of a sparse frame structure and receiver processing based on sparse dictionary learning.…”

Section: Other Machine Learningmentioning

confidence: 99%

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mahdi

Ahmad

Qassim³

et al. 2021

Applied Sciences

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Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

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“…In addition, an application-specific NOMA-based communication architecture was investigated for future URLLC Tactile Internet [22]. In [14], [15], [23] and [24], the authors presented coordinated and uncoordinated access protocols to support massive connectivity in massive MIMO systems by exploiting large spatial degrees of freedom to admit massive IoT devices. Specifically, the authors in [14] and [15] discussed that the massive MIMO system can be acted as a natural enabler for URLLC where multiple antenna systems can support high capacity, spatial multiplexing and diversity links.…”

Section: A Related Workmentioning

confidence: 99%

Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications

Yang

Xiong

Zhao

et al. 2020

Preprint

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With the rapid deployment of the Internet of Things (IoT), fifth-generation (5G) and beyond 5G networks are required to support massive access of a huge number of devices over limited radio spectrum radio. In wireless networks, different devices have various quality-of-service (QoS) requirements, ranging from ultra-reliable low latency communications (URLLC) to high transmission data rates. In this context, we present a joint energy-efficient subchannel assignment and power control approach to manage massive access requests while maximizing network energy efficiency (EE) and guaranteeing different QoS requirements. The latency constraint is transformed into a data rate constraint which makes the optimization problem tractable before modelling it as a multi-agent reinforcement learning problem. A distributed cooperative massive access approach based on deep reinforcement learning (DRL) is proposed to address the problem while meeting both reliability and latency constraints on URLLC services in massive access scenario. In addition, transfer learning and cooperative learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. Simulation results clearly show that the proposed distributed cooperative learning approach outperforms other existing approaches in terms of meeting EE and improving the transmission success probability in massive access scenario.

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Massive Uncoordinated Access With Massive MIMO: A Dictionary Learning Approach

Cited by 38 publications

References 39 publications

Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO

Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications

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